The human brain is capable of processing information about events and predicting other events that will result from the original event. For example, if a person sees a fragile object falling off of a table, the person can predict that the object will, if left alone, fall to the ground and be damaged. Similarly, if a catastrophe occurs, such as an earthquake, the human mind can predict that there will be damage to buildings and possibly the need for aid, such as shelter and food supplies. While this type of event prediction seems trivial, it is actually the result of the complex learning ability of the human mind and the ability to process data against causality rules learned from previous experience. This process of observing an event and reasoning about future events that may result from the observed event, known as “causal reasoning”, is a very complex task that can be performed quickly on a small scale by the human brain.
Where the human mind is limited, as compared to digital computers, is in its ability to quickly process large volumes of data. For example, a person could not possibly digest the information in every newspaper, let alone the myriad of other information sources, such as television, web pages, social media, and the like. To date, event prediction has been limited by the ability of the human mind to process data and the inability of digital computers to correlate data and learn causality in a scalable manner.
In the paper entitled Learning Causality for News Events, the authors propose a system for predicting events by extracting causal relationships based on language cues such as: A “resulted in” B. Cause and event pairs are thus determined. Complex ontologies are required to develop the cause and event pairs. It is also known how to code event descriptions in various ways for processing. For example, Conflict and Media Event Observations (CAMEO) discloses an event framework coding scheme for studying mediation of international disputes.
It has been posited that significant societal events are somewhat predictable based on indicators in societies' communications, activities, and consumption prior to an event. For example, political unrest, social trends, population needs and the like could be predicted in advance. These indicators can be found in the vast amount of communications and activities that are documented in the online world. For example, news reports, social media communications, economic statistics and transactions are all recorded on the Internet and other sources. However, the volume of this data is enormous and most of the data is meaningless in the context of event prediction. As a result, there have been no real-time scalable algorithms for predicting events.